The physicochemical properties of engineered nanomaterials (ENMs) (such as mobility, quantum size effects, surface area, and surface energy) differ substantially from those of the corresponding bulk materials sharing the same chemical composition and atomic structure. These properties endow ENMs with exceptional conductivity, reactivity and optical sensitivity, and hence superior functionality in consumer products such as sporting goods, tires, catalysts, microelectronics, cleaners, paints, cosmetics and pharmaceuticals. On the other hand, these same properties also result in interactions of ENMs with biological systems that vary from those of their micron-sized counterparts, potentially making nanomaterials unsafe for humans and for the environment (Nel et al., 2006, Science 311:622-627).
The most common exposure pathways for these ENMs include ingestion (e.g., pharmaceutical products and food), dermal contact (e.g., occupational exposure and cosmetics), injection (e.g., nanomedicines and drug delivery mechanisms) and inhalation (e.g., occupational and consumer exposure), potentially resulting in diverse toxicological outcomes. For example, recent studies suggest that inhaled nanoparticles may pass from the lungs into the bloodstream and enter extrapulmonary organs, leading to increased cardiovascular morbidity and mortality (Choi et al., 2010, Nat. Biotechnol. 28:1300-1303; Brain, 2009, Nanotoxicol. 3:7; Mills et al., 2009, Nat. Clin. Pract. Cardiovasc. Med. 6:36-44). Although preliminary evidence demonstrates the potential for ENMs to cause adverse biological effects, the underlying toxicity mechanisms are not currently well understood (Oberdorster, 2007, Nanotoxicol. 1:24).
Attempts have been made to develop efficient and inexpensive screening tools to correlate mechanisms of biological activity and toxicity with ENM characteristics, such as size, shape and surface area (Rallo et al., 2011, Environ. Sci. Technol. DOI: 10.1021/es103606x; Shaw et al., 2008, Proc. Natl. Acad. Sci. USA 105:7387-7392; Krewski et al., 2010, J. Toxicol. Environ. Health B Crit. Rev. 13:51-138). Due to the high cost and time required for in vivo toxicity studies, most of these efforts have focused on in vitro methods (Lai, 2011, Nanomed. Nanobiotechnol. DOI:10.1002/wnan.162; Balbus et al., 2007, Environ. Health Perspect. 115:1654-165). High-throughput in vitro toxicity assays have recently been employed to assess toxicity end points, in various cell lines, for libraries of ENMs over a range of exposure times and concentrations (George, 2011, ACS Nano 5:13).
In addition to the refinement and standardization of in vitro and in vivo methods, delivery of ENMs in liquid suspension to cultured cells (a typical procedure in in vitro toxicity studies) requires further analysis. First, commercial ENM nanopowders have limited diversity in terms of physicochemical and morphological properties, hampering the systematic parametric study of the relationships between biological outcomes and ENM properties (such as size, surface, composition, shape, and charge). Second, ENMs suspended in culture media may flocculate, agglomerate, dissolve, or even interact with serum components (Fadeel, 2010, Adv. Drug Deliv. Rev. 8:9; Jones & Grainger, 2009, Adv. Drug Deliv. Rev. 61:438-456; Verma & Stellacci, 2010, Small 6:12-21), thus assuming distinct biological properties. More importantly, administered doses may differ substantially from the doses actually delivered to cells. Comparison of in vitro doses to those administered by inhalation is difficult, resulting in large differences in effective dose between in vitro and in vivo studies. Taken together, these limitations may explain some of the disparities reported in the literature between in vivo and in vitro ENM studies (Fadeel, 2010, Adv. Drug Deliv. Rev. 8:9; Fischer & Chan, 2007, Curr. Opin. Biotechnol. 18:565-571).
Typical comparisons of biological response to ENM exposure do not take into account particle-particle and particle-physiologic fluid interactions in the liquid suspension (Oberdorster et al., 2005, Environ. Health Perspect. 113:823-839; Jiang, 2008, Nanotoxicol. 2:10; Rushton et al., 2010, J. Toxicol. Environ. Health A 73:445-461; Wittmaack, 2007, Environ. Health Perspect. 115:8; Oberdorster et al., 1994, Environ. Health Perspect 102(Suppl. 5):173179). Such interactions depend largely on the dispersion protocol; the particle characteristics (including primary particle size and shape, chemical composition and surface chemistry) (Ji et al., 2010, Environ. Sci. Technol. 44:7309-7314; Jiang, 2009, J. Nanopart. Res. 11:13; Murdock et al., 2008, Toxicol. Sci. 101:239-253; Zook et al., 2010, Nanotoxicol. DOI: 10.3109/17435390.2010.536615); and the liquid media properties (such as ionic strength, specific conductance, pH and protein content) (Lee et al., 2011, J. Comp. Neurol. 519:34-48; Bihari et al., 2008, Part. Fibre Toxicol. 5:14; Elzey, 2009, J. Nanopart. Res. 12:14; Murdock et al., 2008, Toxicol. Sci. 101:239-253; Zook et al., 2010, Nanotoxicol. DOI: 10.3109/17435390.2010.536615; Wiogo et al., 2011, Langmuir 27:843-850; Laxen, 1977, Water Res. 11:4).
ENM interactions, in turn, lead to agglomeration in liquid media, altering the total number of free particles in suspension and the total surface area available for interaction with cells in vitro, as well as the mass transport of ENMs (i.e., sedimentation and diffusion coefficients), which directly impacts delivery of particles to cells (Teeguarden et al., 2007, Toxicol. Sci. 95:300-312). For example, rapidly settling particles elicit cytokine secretion within minutes of application, whereas slow or non-settling particles may take several hours to elicit a similar response (Teeguarden et al., 2007, Toxicol. Sci. 95:300-312). Further, the methods used to disperse nanoparticles in culture media for in vitro studies, which can substantially affect their physical and chemical properties—and hence their biological activities—differ widely (Roco, 2011, J. Nanoparticle Res. 13(3):897-919). Clearly a harmonized (standardized and shared) protocol for nanoparticle dispersion is required to ensure that congruous and cumulative data become available.
The extent of agglomeration of ENMs in a fluid may be controlled by adopting dispersion protocols that include identification of the critical dispersion energy needed to generate the smallest possible agglomerate sizes and distributions that are optimally stable over time (Cohen et al., 2012, Nanotoxicol. doi:10.3109/17435390.2012.666576; Taurozzi, “Protocol for Preparation of Nanoparticle Dispersions from Powdered Material Using Ultrasonic Disruption”, CEINT/NIST, 2010; Taurozzi et al., 2012, Nanotoxicol. doi:10.3109/17435390.2012.665506). In addition to a stable and well-characterized suspension, accurate modeling of ENM transport requires accurate calculation of agglomerate size and shape, as well as the heretofore elusive property of density.
Particle transport in static uniform solutions at constant temperature (conditions inherent to in vitro systems) is primarily driven by diffusion and sedimentation (Teeguarden et al., 2007, Toxicol. Sci. 95(2):300-12; Cho et al., 2011, Nat. Nanotechnol. 6(6):385-91; Hinderliter et al., 2010, Part. Fibre Toxicol. 7(1):36). For the purposes of modeling particle transport in an in vitro system, diffusion and sedimentation velocities can be estimated based on the following equations. The Stokes-Einstein equation defines the diffusion coefficient (D, m2/s) as:
                    D        =                                            k              B                        ⁢            T                                3            ⁢                                                  ⁢            π            ⁢                                                  ⁢            μ            ⁢                                                  ⁢            d                                              (        1        )            where kB is the Boltzmann constant (Pa·m3·K−1), μ is the media dynamic viscosity (Pa·s), and d is the particle diameter (m) in suspension.
The sedimentation velocity of a particle in suspension, derived from the frictional drag force (defined by Stokes' Law), buoyant force and gravitational force acting upon it is defined as
                              v          =                                                    g                ⁡                                  (                                                            ρ                      E                                        -                                          ρ                      media                                                        )                                            ⁢                              d                2                                                    18              ⁢                                                          ⁢              μ                                      ,                            (        2        )            where g is acceleration due to gravity (m/s2), ρE is particle effective density (g/cm3), ρmedia is media density (g/cm3), d is the diameter of the particle in solution (m), and μ is the media viscosity (Pa·s).
From these equations, it is clear that agglomeration can influence particle transport by altering particle size, as well as by altering particle effective density. Typically, ENM agglomerates in liquid suspension are not composed of efficiently packed particles with zero porosity and do not have densities equal to that of the raw material (ρp). Rather, ENMs exhibit “chain like” fractal structures and are porous, resulting in the formation of protein coronas and entrapment of liquid media within the agglomerate. As a result, the effective density of the resulted agglomerate, (ρE), can be significantly lower than that of the raw material (ρp), and may be closer to the density of the media (ρmedia).
Current methods for empirically measuring the effective density of ENM agglomerates in liquid suspension are time consuming and require specialized and expensive equipment. Analytical ultracentrifugation measurements can take up to several days per sample and often fail due to samples leaking from cell housing units, making characterization of a large panel of ENMs a prolonged and expensive endeavor (Schulze et al., 2008, Nanotoxicology 2(2):11; Mittal & Lechner, 2010, J. Colloid Interface Sci. 346(2):378-83; Zook et al., 2011, ACS Nano 5(10):8070-9). Other studies have reported and based modeling and dose calculations on effective densities estimated from complex models that rely on best-guess values for a number of ENM-specific parameters, including the number of particles per agglomerate, the specific fractal dimension of the ENM (DF) and packing factor (PF) (Cohen et al., 2012, Nanotoxicol. doi:10.3109/17435390.2012.666576; Hinderliter et al., 2010, Part. Fibre Toxicol. 7(1):36). The fractal dimension contributes substantially to the broad hypothetical range of ENM effective density, having values between 1 and 3, where a value of 3 indicates a perfect compact sphere with zero porosity. Packing factor values contribute additional uncertainty, theoretically varying from 0 to 1, where a value of 1 indicates agglomerates are efficiently packed with zero porosity (Hinderliter et al., 2010, Part. Fibre Toxicol. 7(1):36; Sterling et al., 2005, Water Res. 39(9):1818-30). The ENM-specific values for DF and PF are not well known and have not been validated in experimental studies. For metal oxides, estimated best-guess values of 2.3 and 0.637 for DF and PF, respectively, have been used in the literature (Cohen et al., 2012, Nanotoxicol. doi:10.3109/17435390.2012.666576; Hinderliter et al., 2010, Part. Fibre Toxicol. 7(1):36; Sterling et al., 2005, Water Res. 39(9):1818-30).
From the foregoing discussion, it is clear that there is a need in the art for methods that allow empirical measurement of effective density of ENM agglomerates in liquid suspension. Such methods could be used to correlate ENM properties with their biological activity and toxicity. The present invention addresses this need.